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Advantage of Entangled Learning Rules in Quantum Measurement Class Learning

arXiv:2610.07328v1 Announce Type: cross Abstract: Learning with data in the form of quantum states is of current interest and has led to a variety of problems that boil down to interaction with the av

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Adversarial Training for Deep Hedging in Nonstationary Markets

DGX agent

arXiv:2610.07162v1 Announce Type: new Abstract: Deep hedging learns trading policies from historical or simulated market trajectories, yet under nonstationarity these training paths may not represent

researcharxiv-cs-lg

7 Oct 2026

Adversarially Trained Linear Transformers Are Optimal Robust In-Context Learners for Gaussian Mixtures

arXiv:2610.07754v1 Announce Type: cross Abstract: Adversarial training is one of the most reliable defenses against adversarial attacks, but its high computational cost must generally be paid anew for

Agreement Is Not Validity: Cross-Model LLM Consensus in Diagnosing Student Failure Modes in K-12 Math Tutoring Dialogue

arXiv:2610.08703v1 Announce Type: new Abstract: In K-12 mathematics tutoring, student-tutor dialogue provides rich evidence of learners' problem-solving processes and sources of difficulty. Learning a

AIMS: Anchor-Integrated Multi-View Synthesis for Scalable Novel View Rendering

arXiv:2610.07566v1 Announce Type: new Abstract: Feed-forward novel view synthesis methods achieve strong generalization from posed multi-view inputs, but scaling them to large input view sets remains

Align, Then Correct: Training-Free Two-Stage Low-Rank Compensation for Extremely Quantized Large Language Models

arXiv:2610.08164v1 Announce Type: cross Abstract: Low-rank quantization error compensation (LQEC) recovers the accuracy lost under aggressive weight quantization by attaching a closed-form rank-r adap

An overview of machine learning-enhanced iterative methods for systems of linear and nonlinear equations

arXiv:2610.07211v1 Announce Type: cross Abstract: Systems of equations arise in a wide range of scientific and engineering applications. The present work focuses on solvers for general systems of equa

Anchor Divergence for Semantic Geometry in Contrastive Learning

arXiv:2610.06919v1 Announce Type: new Abstract: This paper concerns how semantic context determines geometry in learned vector representations. Similarity is typically measured using cosine similarity

AnyBottle: A Recipe to Only Keep the Concepts You Really Need

arXiv:2610.08552v1 Announce Type: new Abstract: Concept bottleneck models (CBMs) make predictions inspectable and intervenable by routing them through human-interpretable concepts, but originally requ

Anytime-valid simulation-based hypothesis testing

arXiv:2610.08210v1 Announce Type: cross Abstract: For a given data distribution (X_t)_{t in N} sim Q i.i.d., we investigate the hypothesis testing problem: H_0: Q = P_0 vs. H_1: Q = P_1, for two diffe

APEX: Speculate smarter, not deeper

arXiv:2610.07780v1 Announce Type: new Abstract: Speculative decoding reduces large language model inference latency by drafting multiple tokens before target-model verification, but its effectiveness

ApexQuant: Data-Free Elastic Quantization by Residual Re-Isotropization

arXiv:2610.07904v1 Announce Type: new Abstract: We introduce ApexQuant, a calibration-free quantization method that recursively re-quantizes the residual error, serving as a refinement layer on top of

Are Language Models Script-Aware?

arXiv:2610.08037v1 Announce Type: new Abstract: Language models frequently generate outputs in unintended languages or scripts, a phenomenon known as off-target generation. While existing research has

Ariadne's Thread of LipSync: Unraveling Forgeries via Inconsistency between Lip Motions and Head Poses

arXiv:2610.08417v1 Announce Type: new Abstract: Recent advances in LipSync generation technology have led to the creation of highly realistic videos, posing severe societal risks. However, existing de

Atom-JEPA: Joint-Embedding Predictive Architecture for 3D Atomistic Systems

arXiv:2610.08400v1 Announce Type: cross Abstract: Large-scale self-supervised pretraining has reshaped modern machine learning, substantially advancing the ability of language and vision models to gen

Attenuated in-context identification in time-series foundation models: diagnosis under counterfactual inputs and repair by synthetic forced-system fine-tuning

arXiv:2610.08118v1 Announce Type: new Abstract: Covariate-aware time-series foundation models (TSFMs) promise training-free what-if answers for instrumented plants: the change in output that a differe

AutodidactWAM: Cross-Modal Self-Distillation from Generated Video to Robot Actions

arXiv:2610.08119v1 Announce Type: new Abstract: World-action models (WAMs) such as Cosmos 3 jointly generate future video and robot actions from an observation and instruction. Adapting one such model

Backend-Agnostic Sparse Attention for Fast High-Resolution Visual Generation

arXiv:2610.08772v1 Announce Type: new Abstract: Diffusion Transformers (DiTs) have achieved strong performance in image and video generation, but the quadratic complexity of full attention makes high-

Beyond Successor Accuracy: State Retention for Recursive Self-Improvement in Recommendation

arXiv:2610.07105v1 Announce Type: cross Abstract: Recommendation recursive self-improvement (Rec-RSI) feeds recommender outputs into subsequent training. Evaluating each round solely through its lates

Beyond Task Reward: A Controller-Restriction Protocol for Evaluating Embodiment-Dependent Competence

arXiv:2610.07629v1 Announce Type: new Abstract: Co-design methods optimize a robot's body and controller jointly and judge the result by one number, the task reward of the fully optimized pair. That n

Beyond the Linear Representation Hypothesis: Non-Linear Activation Steering in Text-to-Image Models

arXiv:2610.06945v1 Announce Type: new Abstract: Mechanistic interpretability often relies on the Linear Representation Hypothesis (LRH), which assumes that high-level concepts are encoded as linear di

Boosting Large Language Models with Mask Fine-Tuning

arXiv:2503.22764v3 Announce Type: replace-cross Abstract: The large language model (LLM) is typically integrated into the mainstream optimization protocol. However, it remains underexplored whether ma

BRACE: Adapting Whole-Body References for Force and Terrain Aware Humanoid Motion Tracking

arXiv:2610.07052v1 Announce Type: new Abstract: Whole-body tracking has become the interface through which operators drive humanoid robots, yet the references it consumes are recorded on level ground

Building Rome from a Single Image

arXiv:2610.08790v1 Announce Type: new Abstract: Single-image scene generation aims to produce a complete 3D scene mesh from a single image, including surfaces the camera did not observe. While pretrai

Calibrated Uncertainty for Informative Path Planning in Aquatic Environmental Monitoring

arXiv:2609.34577v2 Announce Type: replace Abstract: Informative Path Planning for scalar field reconstruction uses predictive uncertainty to direct sensing vehicles toward maximally informative locati

CALR: Continuous Anchored Latent Reasoning via Render-of-Thought Compression

arXiv:2610.07175v1 Announce Type: new Abstract: Visual latent reasoning compresses rendered derivations into compact intermediate states, reducing textual reasoning overhead. Existing approaches diffe

Can LLM-assisted regularization increase forecast accuracy for migration flows in low data regimes?

arXiv:2610.07208v1 Announce Type: new Abstract: Predicting migration flows remains a significant challenge for traditional gravity-based forecasting models, which primarily rely on structured socio-ec

Can phenotypic activity be predicted without experimental readouts?

arXiv:2610.07997v1 Announce Type: new Abstract: Molecular encoders contrastively pretrained on paired molecule-morphology data, such as CLOOME and CellCLIP, have been proposed as cheap surrogates for

Can We Model the Artifacts Explicitly? Disentangle Artifacts via Pairwise Edit Relations for Image Manipulation Localization

arXiv:2610.07916v1 Announce Type: new Abstract: Image Manipulation Localization (IML) is commonly formulated as a fully supervised learning task that estimates the optimal manipulation mask y for a gi

CANDLE: Cortical Null-Space Decomposition for Noninvasive Brain Source Imaging

arXiv:2610.07824v1 Announce Type: new Abstract: Electrophysiological source imaging (ESI) aims to estimate cortical source activity from noninvasive electrophysiological measurements such as electroen

Catastrophic Forgetting in Sequential Thermal Anti-UAV Detection: The Role of Scale-Conditioned Gradient Imbalance

arXiv:2610.08315v1 Announce Type: new Abstract: Counter-UAV systems based on thermal infrared detection must stay accurate as operational datasets evolve, yet sequential fine-tuning causes catastrophi

Causal Effect Estimation under Networked Interference without Networked Unconfoundedness Assumption

arXiv:2502.19741v4 Announce Type: replace Abstract: Estimating causal effects under networked interference from observational data is a crucial yet challenging problem. Most existing methods mainly re

CHARTER: Auditing Reference Substitution in Hierarchical Compact-Evidence Evaluation for Computational Pathology

arXiv:2610.07843v1 Announce Type: new Abstract: In digital pathology, compact evidence is often used to explain or audit predictions made by whole-slide image multiple instance learning models. In hie

Closing Ambient Clinical Documentation Gaps with Automated Provider Queries

arXiv:2610.07502v1 Announce Type: new Abstract: Provider queries are clarifying requests sent by clinical documentation specialists to physicians to close gaps in the clinical note and ensure accurate

Common-Mode Errors Limit Low-Timestep Deep Spiking Q-Networks

arXiv:2610.07808v1 Announce Type: cross Abstract: Spiking neural networks (SNNs) offer sparse and event-driven computation, making them attractive for energy-constrained reinforcement learning (RL) on

Communication-Free Obstacle Localization from Aggregate Wrench Measurements in Leader--Follower Cooperative Transport

arXiv:2610.08324v1 Announce Type: new Abstract: We consider obstacle localization for a team of robots cooperatively transporting a rigid payload without explicit inter-robot communication. A leader r

Comparative review of hybrid forecasting models for short-term prediction of building thermal load

arXiv:2610.06881v1 Announce Type: cross Abstract: In this paper, a comparative review of different hybrid models for short-term forecasting of building thermal demand is carried out. Particularly, the

COMPASS: Finding Where Reasoning Lives in Language Models

arXiv:2610.07469v1 Announce Type: new Abstract: Explicitly eliciting reasoning substantially improves LLM performance. Existing approaches require a predefined characterization of reasoning, whether t

Complementary Feature Domains: Information Preservation Does Not Imply Predictive-Contribution Preservation

arXiv:2610.07565v1 Announce Type: cross Abstract: Complementary Feature Domains (CFD) theory characterizes predictive value as a context-indexed contribution system induced jointly by representations

Complementary Supervised and Self-Supervised Representations for Out-of-Distribution Graph Learning

arXiv:2610.07628v1 Announce Type: new Abstract: Out-of-distribution (OOD) generalization remains challenging for graph neural networks (GNNs), as graph distributions can vary substantially across time

Component and Dimension Sparsity in Transformer Refusal Mechanisms

arXiv:2610.06903v1 Announce Type: cross Abstract: Activation steering manipulates large language model behavior by intervening on internal activations, but the mechanistic basis of these interventions

Compositional Concept Erasure in Text-to-Image Diffusion Models via Hierarchically Grounded Semantic Surgery

arXiv:2610.07337v1 Announce Type: new Abstract: Removing copyrighted, unsafe, or user-specified concepts from a deployed text-to-image diffusion model is now a practical requirement. Weight-editing me

Comprehensive Evaluation and Fine-Tuning of Foundational Cell Nuclei Segmentation Models in Renal Pathology

arXiv:2610.07711v1 Announce Type: new Abstract: Accurate nuclei instance segmentation is essential for quantitative renal pathology, yet general-purpose models often struggle with low contrast, dense

Confidence-Ordering Reversal under Contextual Priors in Neural Decoding

arXiv:2610.08229v1 Announce Type: new Abstract: Contextual priors improve neural-to-language decoding by reshaping candidate scores. However, confidence is read from the same reshaped scores, so the e

Conformal Prediction Sets Quantify Information Gain: A Theoretical Perspective

arXiv:2610.08785v1 Announce Type: new Abstract: Conformal prediction is a popular tool for uncertainty quantification that outputs prediction sets with finite-sample coverage guarantees. While predict

Continuous Memory Machines

arXiv:2610.07907v1 Announce Type: new Abstract: Recurrent neural networks typically compress information into a single vector-valued recurrent state, forcing short-term computation and long-term reten

Convergence of Sharpness-Aware Minimization Algorithms using Increasing Batch Size and Decaying Learning Rate

arXiv:2409.09984v2 Announce Type: replace Abstract: The sharpness-aware minimization (SAM) algorithm and its variants, including gap guided SAM (GSAM), have been successful at improving the generaliza

Conversation Is a Two-Body Problem: Dyadic Evaluation of Full-Duplex Dialogue Models

arXiv:2610.08125v1 Announce Type: cross Abstract: Full-duplex spoken dialogue models listen and speak at the same time, enabling voice agents to have natural, low-latency interactions that turn-based